Insulation optimization of aluminum profile for high-speed train based on RBF neural network model of sound insulation

Insulation optimization of aluminum profile for high-speed train based on RBF neural network model of sound insulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Insulation optimization of aluminum profile for high-speed train based on RBF neural network model of sound insulation Yuxiang Hao, Leiming Song, Hanyang Yu, Xiaojun Hu, Hao Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5352444/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Improving the sound insulation of the car body structure is an important technical means to reduce the noise inside the high-speed train. Without reducing the sound insulation of the structure, lowering the mass of the sound insulation structure is an important way to the lightweight of the high-speed train. The model based on radial basis function neural network (BRF) is established by taking the parameters such as the upper plate, the lower plate, the rib plate, the thickness of the aluminum profile and the angle of the rib plate as the design factors, and the weighted sound insulation Rw and the mass of the aluminum profile as the output response. The RBF model is used to optimize the sound insulation performance of the aluminum profile by using the hybrid algorithm of Multi-island Genetic Algorithm (MIGA) and nonlinear programming quadratic line (NLPQL), which greatly improves the optimization efficiency of the sound insulation performance of the aluminum profile. The sidewall aluminum profile of a high-speed train is optimized, and the Rw is increased by 3.6dB under the condition of constant mass. The mass is reduced by 18.5% when the R w is constant. High-speed train Aluminum profile RBF neural network model Hybrid algorithm optimization Sound insulation performance optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5352444","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":373591456,"identity":"2f8d65e3-82fb-42bb-877e-f7ebe503993c","order_by":0,"name":"Yuxiang Hao","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Yuxiang","middleName":"","lastName":"Hao","suffix":""},{"id":373591457,"identity":"dfc593be-d075-46fa-a648-fbe0e4fe9b0b","order_by":1,"name":"Leiming Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACPghlk8DYAKLZiNACVZOWwNhGopbDCRAWUVrEjj98XPDrfB7z/B4Dhg9lhxn4ZzcQ0CKdY2w8s+92MWMbjwHjjHOHGSTuHCCohU2at+d2YiNQCzNv22EGA4kEQlrSnwG1nINo+UuclgQzaZ4fByBaGInTAvQLb0My0C9pBQd7zqXzSNwgoIVfOv3hY54/dnmGzYc3PvhRZi3HP4OAFjAARaNhAwPDASCbhwj1IPCHgUGeSKWjYBSMglEwAgEAJqc90k0w0x4AAAAASUVORK5CYII=","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Leiming","middleName":"","lastName":"Song","suffix":""},{"id":373591458,"identity":"98a5842f-80e5-46a3-9001-f0176416fa69","order_by":2,"name":"Hanyang Yu","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Hanyang","middleName":"","lastName":"Yu","suffix":""},{"id":373591459,"identity":"ba31404c-da5a-4427-b7c8-fc0780eefb1f","order_by":3,"name":"Xiaojun Hu","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojun","middleName":"","lastName":"Hu","suffix":""},{"id":373591460,"identity":"8ddd5388-c5b5-45f5-a776-0c2c1508da3c","order_by":4,"name":"Hao Lin","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2024-10-29 08:23:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5352444/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5352444/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69116013,"identity":"7ef4c170-f308-4803-a581-8acee1361d08","added_by":"auto","created_at":"2024-11-15 22:46:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":742818,"visible":true,"origin":"","legend":"","description":"","filename":"InsulationoptimizationofaluminumprofileforhighspeedtrainbasedonRBFneuralnetworkmodel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5352444/v1_covered_3b4bc1c9-7d5e-41d0-89b6-01c1a602087d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Insulation optimization of aluminum profile for high-speed train based on RBF neural network model of sound insulation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"High-speed train, Aluminum profile, RBF neural network model, Hybrid algorithm optimization, Sound insulation performance optimization","lastPublishedDoi":"10.21203/rs.3.rs-5352444/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5352444/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eImproving the sound insulation of the car body structure is an important technical means to reduce the noise inside the high-speed train. Without reducing the sound insulation of the structure, lowering the mass of the sound insulation structure is an important way to the lightweight of the high-speed train. The model based on radial basis function neural network (BRF) is established by taking the parameters such as the upper plate, the lower plate, the rib plate, the thickness of the aluminum profile and the angle of the rib plate as the design factors, and the weighted sound insulation Rw and the mass of the aluminum profile as the output response. The RBF model is used to optimize the sound insulation performance of the aluminum profile by using the hybrid algorithm of Multi-island Genetic Algorithm (MIGA) and nonlinear programming quadratic line (NLPQL), which greatly improves the optimization efficiency of the sound insulation performance of the aluminum profile. The sidewall aluminum profile of a high-speed train is optimized, and the Rw is increased by 3.6dB under the condition of constant mass. 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